We study the problem of shape generation in 3D mesh representation from a few
color images with known camera poses. While many previous works learn to
hallucinate the shape directly from priors, we resort to further improving the
shape quality by leveraging cross-view information with a graph convolutional
network. Instead of building a direct mapping function from images to 3D shape,
our model learns to predict series of deformations to improve a coarse shape
iteratively. Inspired by traditional multiple view geometry methods, our
network samples nearby area around the initial mesh's vertex locations and
reasons an optimal deformation using perceptual feature statistics built from
multiple input images. Extensive experiments show that our model produces
accurate 3D shape that are not only visually plausible from the input
perspectives, but also well aligned to arbitrary viewpoints. With the help of
physically driven architecture, our model also exhibits generalization
capability across different semantic categories, number of input images, and
quality of mesh initialization.

使用图卷积网络从多角度图像中预测一系列形状变形，以进一步提高 3D 网格表征的形状质量，从而获得高精确度、高可靠性且具有普适性的 3D 模型。

Pixel2Mesh++: 多视角下基于变形的 3D 网格生成

Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation

We introduce Scan2Mesh, a novel data-driven generative approach which
transforms an unstructured and potentially incomplete range scan into a
structured 3D mesh representation. The main contribution of this work is a
generative neural network architecture whose input is a range scan of a 3D
object and whose output is an indexed face set conditioned on the input scan.
In order to generate a 3D mesh as a set of vertices and face indices, the
generative model builds on a series of proxy losses for vertices, edges, and
faces. At each stage, we realize a one-to-one discrete mapping between the
predicted and ground truth data points with a combination of convolutional- and
graph neural network architectures. This enables our algorithm to predict a
compact mesh representation similar to those created through manual artist
effort using 3D modeling software. Our generated mesh results thus produce
sharper, cleaner meshes with a fundamentally different structure from those
generated through implicit functions, a first step in bridging the gap towards
artist-created CAD models.

Scan2Mesh 是一种基于数据驱动的生成方法，其通过神经网络实现将非结构化和可能不完整的 3D 扫描转换成结构化的 3D 网格表示，并生成了类似于手工建模软件创建的 CAD 模型的网格表示。